1. Technical Field
The present invention relates to virtual endoscopy.
2. Discussion of the Related Art
The second leading cause of cancer-related deaths in the United States is colorectal cancer. Unfortunately, it is most often discovered after the patient has developed symptoms. It is recommended that adults should be screened to detect cancer-related polyps. The traditional screening using optical colonoscopy, however, is invasive, expensive, time-consuming, and uncomfortable, and requires an intensive bowel preparation. Because of this, many are not screened. Virtual colonoscopy (VC), also known as computed tomographic colonography (CTC), has been developed to help encourage adults to be regularly screened for polyps. In VC fly-through navigation, it is crucial to generate an optimal camera path for efficient colonic polyp screening. Automatic path planning is required by a VC system because manual planning is difficult and time-consuming due to the complex shape of the human colon. For complete and accurate diagnosis, a planned path should not produce significant blind areas on the colon surface.
There has been a great deal of research on navigation methods for three-dimensional (3D) virtual endoscopy, which can be classified into three categories: manual navigation [M. Gleicher and A. Witkin, “Through the lens camera control”, in Proc. ACM SIGGRAPH '92, pp. 331-340, 1992 and R. Turner, F. Balaguer, E. Gobbetti and D. Thalmann, “Physically-based interactive camera motion control using 3D input devices”, in Computer Graphics International '91, pp. 135-145, 1991], planned navigation [L. Hong, A. Kaufman, Y. Wei, A Viswambharan, M. Wax, and Z. Liang, “3D virtual colonoscopy”, in IEEE Symposium on Biomedical Visualization, pp. 26-32, 1995 and G. Rubin, C. Beaulieu, V. Argiro, H. Ringl, A. Norbash, J. Feller, M. Dake, R. Jeffey, and S. Napel, “Perspective volume rendering of CT and MRI images: Applications for endoscopic imaging”, Radiology 99, pp. 321-330, 1996], and guided navigation [L. Hong, S. Muraki, A. Kaufmann, D. Bartz and T. He, “Virtual voyage: Interactive navigation in the human colon”, in Proc. ACM SIGGRAPH '97, pp. 27-34, 1997, M. Wan, Q. Tang, A. Kaufman, Z. Liang, and M. Wax, “Volume rendering based interactive navigation within the human colon”, in Proc. IEEE Visualization '99, pp. 397-400, 1999 and K. Kwon and B. Shin, “An efficient camera path computation using image-space information in virtual endoscopy”, Lecture Notes in Computer Science 3280, pp. 118-125, 2004]. Manual navigation requires the user to control the camera at every step, which is inefficient and uncomfortable. Moreover, the camera may penetrate through the colon surface when it is incorrectly handled by a physician.
Planned navigation calculates entire camera path and orientations in the preprocessing step, then continuously moves the camera along the pre-calculated path during the navigation. In this method, the physician cannot intuitively change the camera position and orientation. Further, a lot of computation is required in the preprocessing step. The centerline of the colon lumen is usually used as the camera path to obtain a wide view of the colonic surface. Topological thinning methods [L. Hong, A. Kaufman, Y. Wei, A Viswambharan, M. Wax, and Z. Liang, “3D virtual colonoscopy”, in IEEE Symposium on Biomedical Visualization, pp. 26-32, 1995, D. Paik, C. Beaulieu, R. Jeffery, G. Rubin, and S. Napel, “Automated flight path planning for virtual endoscopy”, Medical Physics 25(5), pp. 629-637, 1998, and R. Sadlier and P. Whelan, “Fast colon centerline calculation using optimized 3D topological thinning”, Computerized Medical Imaging and Graphics 29, pp. 251-258, 2005] have been used to eliminate the outermost layer of a segmented colon successively with only the centerline voxels remaining. In the distance mapping method, a distance field is computed, and then the minimum cost spanning tree is built to extract the optimal colonic centerline. Bitter et al. [I. Bitter, M. Sato, M. Bender, K. McDonnel, and A. Kaufman, “CEASAR: A smooth, accurate and robust centerline extraction algorithm”, in Proc. IEEE Visualization '00, pp. 45-52, 2000] have proposed an efficient centerline algorithm using a penalty distance, which is the combination of the distance from the source and the distance from the boundary. Wan et al. [M. Wan, Z. Liang, Q. Ke, L. Hong, I. Bitter, and A. Kaufman, “Automatic centerline extraction for virtual colonoscopy”, IEEE Transactions on Medical Imaging 21(12), pp. 1450-1460, 2002] have used the exact Euclidian distance from each voxel inside the colon lumen to the nearest colon boundary to extract the colon centerline and its associated branches. Hassouna et al. [M. Hassouna and A. Farag, “Robust centerline extraction framework using level sets”, in IEEE Computer Vision and Pattern Recognition, pp. 458-465, 2005] have proposed a robust centerline extraction method, introducing a new speed function of level sets. However, all of these methods are computationally expensive, especially when they are applied to volumetric data.
Guided navigation provides some guidance for the navigation and allows the physician to control it when desired. The potential field [L. Hong, S. Muraki, A. Kaufmann, D. Bartz and T. He, “Virtual voyage: Interactive navigation in the human colon”, in Proc. ACM SIGGRAPH '97, pp. 27-34, 1997 and M. Wan, Q. Tang, A. Kaufman, Z. Liang, and M. Wax, “Volume rendering based interactive navigation within the human colon”, in Proc. IEEE Visualization '99, pp. 397-400, 1999] has been used to determine the camera position and orientation by considering the attractive force directing to the target point, repulsive force from the colon surface, and the external force. It consists of two distance fields inside the colon lumen: distance from the colonic surface and distance from the target point of the current navigation. This method requires additional storage for distance fields, and the computation of the potential field is time consuming. Kang et al. [D. Kang and J. Ra, “A new path planning algorithm for maximizing visibility in computed tomography colonography”, IEEE Transactions on Medical Imaging 24(8), pp. 957-968, 2005] have proposed a method to determine view positions and their view directions to minimize the blind areas during navigation. However, this algorithm showed poor performance to minimize the blind area between haustral folds although the visible areas at the curved regions are increased. Kwon et al. [K. Kwon and B. Shin, “An efficient camera path computation using image-space information in virtual endoscopy”, Lecture Notes in Computer Science 3280, pp. 118-125, 2004] have used image space information generated in rendering time to determine the camera position and direction. This technique does not require preprocessing or extra storage, but it is highly likely to converge to local minima in complex regions.